This is an action-packed specialization is for data science enthusiasts who want to acquire practical skills for real world data problems. It appeals to anyone interested in pursuing a career in Data Science, and already has foundational skills (or has completed the Introduction to Applied Data Science specialization). You will learn Python - no prior programming knowledge necessary. You will then learn data visualization and data analysis. Through our guided lectures, labs, and projects you’ll get hands-on experience tackling interesting data problems. Make sure to take this specialization to solidify your Python and data science skills before diving deeper into big data, AI, and deep learning.
Upon completing all courses in the specialization and receiving the Specialization certificate, you will also receive an IBM Badge recognizing you as a Specialist in Applied Data Science.
LIMITED TIME OFFER: Subscription is only $39 USD per month and gives you access to graded materials and a certificate.

この専門講座には4コースあります。

This introduction to Python will kickstart your learning of Python for data science, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours.
Module 1 - Python Basics
o Your first program
o Types
o Expressions and Variables
o String Operations
Module 2 - Python Data Structures
o Lists and Tuples
o Sets
o Dictionaries
Module 3 - Python Programming Fundamentals
o Conditions and Branching
o Loops
o Functions
o Objects and Classes
Module 4 - Working with Data in Python
o Reading files with open
o Writing files with open
o Loading data with Pandas
o Numpy
Finally, you will create a project to test your skills.
LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!
Topics covered:
1) Importing Datasets
2) Cleaning the Data
3) Data frame manipulation
4) Summarizing the Data
5) Building machine learning Regression models
6) Building data pipelines
Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts:
Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.
If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge.
LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

"A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data.
One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions.
The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.
LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

This capstone project course will give you a taste of what data scientists go through in real life when working with data.
You will learn about location data and different location data providers, such as Foursquare. You will learn how to make RESTful API calls to the Foursquare API to retrieve data about venues in different neighborhoods around the world. You will also learn how to be creative in situations where data are not readily available by scraping web data and parsing HTML code. You will utilize Python and its pandas library to manipulate data, which will help you refine your skills for exploring and analyzing data.
Finally, you will be required to use the Folium library to great maps of geospatial data and to communicate your results and findings.
If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.
LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

講師

IBMについて

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

The specialization consists of 4 courses. Suggested time to complete
each course is 3-4 weeks. If you follow recommended timelines it would
take 3 to 4 months to complete the entire specialization.

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What background knowledge is necessary?

No prior experience in data science or programming is required. However it is recommended that you have some foundational knowledge about data science which can be developed by taking the the Introduction to Applied Data Science specialization by IBM.

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Do I need to take the courses in a specific order?

It is strongly recommended that you take the Python for Data Science course first. Then you can take either the Visualization or the Data Science course - whichever you prefer. And end with the Captsone course.

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専門講座を修了することで大学の単位は付与されますか？

No

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What will I be able to do upon completing the Specialization?

You will be able to learn practical Python skills, and apply them to interesting Data Visualization and Data Analysis problems.